IEEE Trans Med Imaging. 2018 Dec;37(12):2675-2686. doi: 10.1109/TMI.2018.2847250. Epub 2018 Jun 13.
Prior-image-based reconstruction (PIBR) methods have demonstrated great potential for radiation dose reduction in computed tomography applications. PIBR methods take advantage of shared anatomical information between sequential scans by incorporating a patient-specific prior image into the reconstruction objective function, often as a form of regularization. However, one major challenge with PIBR methods is how to optimally determine the prior image regularization strength which balances anatomical information from the prior image with data fitting to the current measurements. Too little prior information yields limited improvements over traditional model-based iterative reconstruction, while too much prior information can force anatomical features from the prior image not supported by the measurement data, concealing true anatomical changes. In this paper, we develop quantitative measures of the bias associated with PIBR. This bias exhibits as a fractional reconstructed contrast of the difference between the prior image and current anatomy, which is quite different from traditional reconstruction biases that are typically quantified in terms of spatial resolution or artifacts. We have derived an analytical relationship between the PIBR bias and prior image regularization strength and illustrated how this relationship can be used as a predictive tool to prospectively determine prior image regularization strength to admit specific kinds of anatomical change in the reconstruction. Because bias is dependent on local statistics, we further generalized shift-variant prior image penalties that permit uniform (shift invariant) admission of anatomical changes across the imaging field of view. We validated the mathematical framework in phantom studies and compared bias predictions with estimates based on brute force exhaustive evaluation using numerous iterative reconstructions across regularization values. The experimental results demonstrate that the proposed analytical approach can predict the bias-regularization relationship accurately, allowing for prospective determination of the prior image regularization strength in PIBR. Thus, the proposed approach provides an important tool for controlling image quality of PIBR methods in a reliable, robust, and efficient fashion.
基于先验图像的重建（PIBR）方法在计算机断层扫描应用中显示出降低辐射剂量的巨大潜力。PIBR 方法利用连续扫描之间的共享解剖学信息，通过将特定于患者的先验图像纳入重建目标函数，通常以正则化的形式，来实现这一目标。然而，PIBR 方法的一个主要挑战是如何最优地确定先验图像正则化强度，以平衡先验图像中的解剖学信息与对当前测量数据的拟合。先验信息太少会导致与传统基于模型的迭代重建相比改进有限，而先验信息太多可能会迫使来自先验图像的解剖学特征与测量数据不匹配，从而掩盖真实的解剖学变化。在本文中，我们开发了用于量化 PIBR 相关偏差的方法。这种偏差表现为先验图像和当前解剖结构之间差异的重建对比度分数，与传统的重建偏差有很大不同，传统的重建偏差通常以空间分辨率或伪影来量化。我们已经推导出 PIBR 偏差与先验图像正则化强度之间的解析关系，并说明了如何将这种关系用作预测工具，以便前瞻性地确定先验图像正则化强度，以允许在重建中接受特定类型的解剖学变化。由于偏差取决于局部统计信息，我们进一步推广了变平移先验图像惩罚，允许在整个成像视场中均匀（平移不变）地接受解剖学变化。我们在体模研究中验证了数学框架，并比较了基于偏置正则化关系的预测与基于大量迭代重建的暴力穷举评估的估计。实验结果表明，所提出的分析方法可以准确地预测偏差-正则化关系，从而可以前瞻性地确定 PIBR 中的先验图像正则化强度。因此，所提出的方法为以可靠、稳健和高效的方式控制 PIBR 方法的图像质量提供了重要工具。
